File size: 1,167 Bytes
ade276c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet50
- cifar10
datasets: cifar10
metrics:
- accuracy
model-index:
- name: resnet50_simclr_cifar10
results:
- task:
type: image-classification
dataset:
name: CIFAR-10
type: cifar10
metrics:
- type: accuracy
value: 0.9012
---
# Model Card for Model ID
This model is a small resnet50 trained on cifar10.
- **Test Accuracy:** 0.9012
- **License:** MIT
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import detectors
import timm
model = timm.create_model("resnet50_simclr_cifar10", pretrained=True)
```
## Training Data
Training data is cifar10.
## Training Hyperparameters
- **config**: `None`
- **model**: `resnet50_simclr_cifar10`
- **batch_size**: `512`
- **epochs**: `501`
- **lr**: `0.5`
- **warmup_epochs**: `10`
- **validation_frequency**: `50`
- **output_features_dim**: `128`
- **seed**: `1`
- **debug**: `False`
- **dataset**: `cifar10`
- **training_mode**: `simclr`
## Testing Data
Testing data is cifar10.
---
This model card was created by Eduardo Dadalto. |